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98 lines
13 KiB
JSON
98 lines
13 KiB
JSON
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"question": {
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"questionId": "1452",
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"questionFrontendId": "1321",
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"categoryTitle": "Database",
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"boundTopicId": 74822,
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"title": "Restaurant Growth",
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"titleSlug": "restaurant-growth",
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"content": "<p>Table: <code>Customer</code></p>\n\n<pre>\n+---------------+---------+\n| Column Name | Type |\n+---------------+---------+\n| customer_id | int |\n| name | varchar |\n| visited_on | date |\n| amount | int |\n+---------------+---------+\nIn SQL,(customer_id, visited_on) is the primary key for this table.\nThis table contains data about customer transactions in a restaurant.\nvisited_on is the date on which the customer with ID (customer_id) has visited the restaurant.\namount is the total paid by a customer.\n</pre>\n\n<p> </p>\n\n<p>You are the restaurant owner and you want to analyze a possible expansion (there will be at least one customer every day).</p>\n\n<p>Compute the moving average of how much the customer paid in a seven days window (i.e., current day + 6 days before). <code>average_amount</code> should be <strong>rounded to two decimal places</strong>.</p>\n\n<p>Return the result table ordered by <code>visited_on</code> <strong>in ascending order</strong>.</p>\n\n<p>The result format is in the following example.</p>\n\n<p> </p>\n<p><strong class=\"example\">Example 1:</strong></p>\n\n<pre>\n<strong>Input:</strong> \nCustomer table:\n+-------------+--------------+--------------+-------------+\n| customer_id | name | visited_on | amount |\n+-------------+--------------+--------------+-------------+\n| 1 | Jhon | 2019-01-01 | 100 |\n| 2 | Daniel | 2019-01-02 | 110 |\n| 3 | Jade | 2019-01-03 | 120 |\n| 4 | Khaled | 2019-01-04 | 130 |\n| 5 | Winston | 2019-01-05 | 110 | \n| 6 | Elvis | 2019-01-06 | 140 | \n| 7 | Anna | 2019-01-07 | 150 |\n| 8 | Maria | 2019-01-08 | 80 |\n| 9 | Jaze | 2019-01-09 | 110 | \n| 1 | Jhon | 2019-01-10 | 130 | \n| 3 | Jade | 2019-01-10 | 150 | \n+-------------+--------------+--------------+-------------+\n<strong>Output:</strong> \n+--------------+--------------+----------------+\n| visited_on | amount | average_amount |\n+--------------+--------------+----------------+\n| 2019-01-07 | 860 | 122.86 |\n| 2019-01-08 | 840 | 120 |\n| 2019-01-09 | 840 | 120 |\n| 2019-01-10 | 1000 | 142.86 |\n+--------------+--------------+----------------+\n<strong>Explanation:</strong> \n1st moving average from 2019-01-01 to 2019-01-07 has an average_amount of (100 + 110 + 120 + 130 + 110 + 140 + 150)/7 = 122.86\n2nd moving average from 2019-01-02 to 2019-01-08 has an average_amount of (110 + 120 + 130 + 110 + 140 + 150 + 80)/7 = 120\n3rd moving average from 2019-01-03 to 2019-01-09 has an average_amount of (120 + 130 + 110 + 140 + 150 + 80 + 110)/7 = 120\n4th moving average from 2019-01-04 to 2019-01-10 has an average_amount of (130 + 110 + 140 + 150 + 80 + 110 + 130 + 150)/7 = 142.86\n</pre>\n",
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"translatedTitle": "餐馆营业额变化增长",
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"translatedContent": "<p>表: <code>Customer</code></p>\n\n<pre>\n+---------------+---------+\n| Column Name | Type |\n+---------------+---------+\n| customer_id | int |\n| name | varchar |\n| visited_on | date |\n| amount | int |\n+---------------+---------+\n在 SQL 中，(customer_id, visited_on) 是该表的主键。\n该表包含一家餐馆的顾客交易数据。\nvisited_on 表示 (customer_id) 的顾客在 visited_on 那天访问了餐馆。\namount 是一个顾客某一天的消费总额。\n</pre>\n\n<p> </p>\n\n<p>你是餐馆的老板，现在你想分析一下可能的营业额变化增长（每天至少有一位顾客）。</p>\n\n<p>计算以 7 天（某日期 + 该日期前的 6 天）为一个时间段的顾客消费平均值。<code>average_amount</code> 要 <strong>保留两位小数。</strong></p>\n\n<p>结果按 <code>visited_on</code> <strong>升序排序</strong>。</p>\n\n<p>返回结果格式的例子如下。</p>\n\n<p> </p>\n\n<p><strong>示例 1:</strong></p>\n\n<pre>\n<strong>输入：</strong>\nCustomer 表:\n+-------------+--------------+--------------+-------------+\n| customer_id | name | visited_on | amount |\n+-------------+--------------+--------------+-------------+\n| 1 | Jhon | 2019-01-01 | 100 |\n| 2 | Daniel | 2019-01-02 | 110 |\n| 3 | Jade | 2019-01-03 | 120 |\n| 4 | Khaled | 2019-01-04 | 130 |\n| 5 | Winston | 2019-01-05 | 110 | \n| 6 | Elvis | 2019-01-06 | 140 | \n| 7 | Anna | 2019-01-07 | 150 |\n| 8 | Maria | 2019-01-08 | 80 |\n| 9 | Jaze | 2019-01-09 | 110 | \n| 1 | Jhon | 2019-01-10 | 130 | \n| 3 | Jade | 2019-01-10 | 150 | \n+-------------+--------------+--------------+-------------+\n<strong>输出：</strong>\n+--------------+--------------+----------------+\n| visited_on | amount | average_amount |\n+--------------+--------------+----------------+\n| 2019-01-07 | 860 | 122.86 |\n| 2019-01-08 | 840 | 120 |\n| 2019-01-09 | 840 | 120 |\n| 2019-01-10 | 1000 | 142.86 |\n+--------------+--------------+----------------+\n<strong>解释：</strong>\n第一个七天消费平均值从 2019-01-01 到 2019-01-07 是restaurant-growth/restaurant-growth/ (100 + 110 + 120 + 130 + 110 + 140 + 150)/7 = 122.86\n第二个七天消费平均值从 2019-01-02 到 2019-01-08 是 (110 + 120 + 130 + 110 + 140 + 150 + 80)/7 = 120\n第三个七天消费平均值从 2019-01-03 到 2019-01-09 是 (120 + 130 + 110 + 140 + 150 + 80 + 110)/7 = 120\n第四个七天消费平均值从 2019-01-04 到 2019-01-10 是 (130 + 110 + 140 + 150 + 80 + 110 + 130 + 150)/7 = 142.86</pre>\n",
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"sampleTestCase": "{\"headers\":{\"Customer\":[\"customer_id\",\"name\",\"visited_on\",\"amount\"]},\"rows\":{\"Customer\":[[1,\"Jhon\",\"2019-01-01\",100],[2,\"Daniel\",\"2019-01-02\",110],[3,\"Jade\",\"2019-01-03\",120],[4,\"Khaled\",\"2019-01-04\",130],[5,\"Winston\",\"2019-01-05\",110],[6,\"Elvis\",\"2019-01-06\",140],[7,\"Anna\",\"2019-01-07\",150],[8,\"Maria\",\"2019-01-08\",80],[9,\"Jaze\",\"2019-01-09\",110],[1,\"Jhon\",\"2019-01-10\",130],[3,\"Jade\",\"2019-01-10\",150]]}}",
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"insert into Customer (customer_id, name, visited_on, amount) values ('1', 'Jhon', '2019-01-01', '100')",
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"insert into Customer (customer_id, name, visited_on, amount) values ('2', 'Daniel', '2019-01-02', '110')",
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"insert into Customer (customer_id, name, visited_on, amount) values ('3', 'Jade', '2019-01-03', '120')",
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"insert into Customer (customer_id, name, visited_on, amount) values ('4', 'Khaled', '2019-01-04', '130')",
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"insert into Customer (customer_id, name, visited_on, amount) values ('5', 'Winston', '2019-01-05', '110')",
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"insert into Customer (customer_id, name, visited_on, amount) values ('6', 'Elvis', '2019-01-06', '140')",
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"insert into Customer (customer_id, name, visited_on, amount) values ('7', 'Anna', '2019-01-07', '150')",
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"insert into Customer (customer_id, name, visited_on, amount) values ('8', 'Maria', '2019-01-08', '80')",
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"insert into Customer (customer_id, name, visited_on, amount) values ('9', 'Jaze', '2019-01-09', '110')",
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"insert into Customer (customer_id, name, visited_on, amount) values ('1', 'Jhon', '2019-01-10', '130')",
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"insert into Customer (customer_id, name, visited_on, amount) values ('3', 'Jade', '2019-01-10', '150')"
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